The economic value of storage in renewable power systems
- the case of thermal energy storage in concentrating solar plants
AUTOREN
Stephan Nagl
Michaela Fürsch
Cosima Jägemann
Marc Oliver Bettzüge
EWI Working Paper, No 11/08
Institute of Energy Economics at the University of Cologne (EWI)
www.ewi.uni-koeln.de
The authors are solely responsible for the contents, which therefore do not necessarily
represent the opinion of EWI.
The economic value of storage in renewable power systems
- the case of thermal energy storage in concentrating solar plantsI
Stephan Nagla,∗, Michaela Fürscha , Cosima Jägemanna , Marc Oliver Bettzügea
a Institute
of Energy Economics, University of Cologne, Vogelsanger Strasse 321, 50827 Cologne, Germany
Abstract
In this article we analyze the value of thermal energy storages in concentrating solar power plants
depending on the electricity generation mix. To determine the value from a system integrated view we
model the whole electricty generation market of the Iberian Peninsula. Key findings for thermal energy
storage units in concentrating solar power plants include an increasing value in electricity systems with
higher shares of fluctuating renewable generation and a potentially significant role in primarily renewable
based electricity systems. Due to the relatively high investment costs concentrating solar power plants with
or without thermal energy storages are not cost efficient in today’s electricity markets. However, expected
cost reductions due to learning curve effects and higher fluctuating renewable generation may lead to a
comparative cost advantage of concentrating solar power plants with thermal energy storages compared to
other renewable technologies.
Keywords: Fluctuating renewables, value of storage, concentrated solar power, power plant optimization
JEL classification: C61, Q40
ISSN: 1862-3808
1. Introduction
As an attempt to fight global warming, many countries try to reduce CO2 emissions from electricity
generation by significantly increasing the share of renewables (RES-E). One major challenge in this transition
I The authors would like to thank the European Academies Science Advisory Council (EASAC) for their suggestions.
Especially Robert Pitz-Paal and Cayetano Lopez supported this analysis with data and helpful comments. This paper also
benefited from comments by the participants of the research seminar of the Institute of Energy Economics at the University of
Cologne in 2011.
∗ Corresponding author
Email address: [email protected], +49 22127729210 (Stephan Nagl)
is the balancing of fluctuating generation by wind or solar technologies and demand against the background
of limited cost-efficient electricity storage options. One technology which might contribute significantly to
solving these problems are concentrating solar power (CSP) plants equipped with thermal storage units
(TES). In CSP plants, the sun’s heat is absorbed by collectors and concentrated to heat a fluid which is
then used to generate electricity in a steam turbine. Specific to CSP systems is the inherent option to
integrate a TES capacity, used to generate electricity in hours with low or no solar radiation. Dependent
on the CSP technology and the site characteristics, TES can even reduce the sites production costs per
kilowatt-hour due to a higher usage of the capital intensive power plant block.
Today, demand in European electricity systems has a midday peak when solar radiation is also highest.
Thus electricity prices are above average when CSP plants can directly feed into the grid. When stored
thermal energy is used to generate electricity, such as during night hours, electricity is often produced in
hours with comparatively low prices.1 However, the structure of the hourly price curve depends on the
specific characteristics of the electricity system. Particularly, a higher share of solar technologies could
reverse the midday peak. Feed-in-tariffs may set, in today’s electricity markets, an inefficient incentive to
invest in thermal storage units for concentrating solar plants as tariffs do not take the hourly price curve
into account.
In this paper, we try to quantify the value of thermal energy storages in CSP plants in today’s electricity
market and the impact of a higher share of fluctuating renewables. The value of electricity storage options
has been analyzed in a number of papers as described in Xi et al. (2011). One of the most commonly
approaches is the so called ‘energy arbitrage’, which essentially analyzes the option of charging storage when
electricity prices are low and discharging when high (e.g. Graves et al. (1999); Sioshansi et al. (2009)). We
use a simulation approach, calibrating the use of CSP to the electricity market of the Iberian Peninsula,
instead of an econometric ‘energy arbitrage’ analysis for several reasons: First, empirical data of high RESE electricity systems are not available yet. Secondly, the investment decision in TES is compared to all
other investment options which contribute to meet demand cost-efficiently. Thirdly, the price curve within
our electricity market model is endogenously determined and the influence of investments in generation or
storage technologies is captured by the structure of the price curve.
1 TES can also be used to shift generation to early evening hours when in some markets demand and thus prices are even
higher than at midday. However, electricity prices are on average higher in hours with high sun radiation.
2
2. Related literature
A number of studies analyze the technical, geographical and economical feasibility of solar energy to
supply a significant share of the electricity demand. This includes the assessment of the technical feasibility
of balancing demand and generation in high-solar scenarios as well as the economic value of CSP and thermal
energy storage technologies both from an investor’s viewpoint and for the economy as a whole.
Sargent and Lundi (2003) and Pitz-Paal et al. (2005) describe the functional principle of different CSP
technologies and thermal energy storage options and assess their future cost development. For example
Sargent and Lundi (2003) expect significant cost reductions for parabolic trough and solar tower technologies
in the mid-term, mainly due to a combination of technological innovation, plant scaling and increased
production volumes. Fthenakis et al. (2009) investigate the technical, geographical and economical feasibility
of solar energy and demonstrate that a significant percentage of electricity demand can be supplied by
photocoltaic and CSP plants in the long-term.
The value of concentrating solar power and thermal energy storage from an investor’s viewpoint has been
examined by Sioshansi and Denholm (2010) and Laing et al. (2010). Sioshansi and Denholm (2010) show that
the addition of thermal energy storages increases the value of CSP plants both by allowing CSP generation
to be shifted to hours with higher energy prices and by increasing the usage of thermal energy from a CSP
plant’s solar field. However, despite these benefits, their results suggest that at current investment costs,
thermal energy storages cannot be economically justified on energy value alone: only if the value of ancillary
service sales and capacity are included, thermal energy storages become cost-effective in a number of cases.
The value of concrete thermal energy storage options for parabolic trough power plants has been assessed
by Laing et al. (2010), taking into account the additional cost of integrating the thermal storage into the
power plant.
In contrast to Sioshansi and Denholm (2010) and Laing et al. (2010), who focus on the value of CSP
systems from an investor’s viewpoint, Poullikkas et al. (2010) investigate the economic costs of integrating
parabolic trough CSP plants in isolated Mediterranean power systems using the example of Cyprus. By
comparing scenarios that differ with respect to new investments in CSP plants (with and without thermal
storage) and natural gas-fired power plants, the study comes to the conclusion that CSP plants with storage
units are the most cost-effective investment option. However, since no other generation options are considered, the results are based on the specific scenario assumptions. Moreover, the results may not be valid for
other power systems, as Cyprus misses for example other storage units such as large pump-storage plants.
3
3. Approach and model description
3.1. Scenario analysis
To analyze the value of thermal energy storage units in CSP plants, we simulate two scenarios with a
dynamic linear investment and dispatch model to determine the cost-minimal electricity mix for the Iberian
Peninsula until 2050. The analysis is conducted for the Iberian Peninsula for several reasons: First, Spain
and Portugal are countries with an annually high solar radiation and secondly Spain has worldwide the
highest installed capacity of CSP plants - a significant number of plants recently commissioned or under
construction include thermal storage units (NREL, 2011). Finally, Spain has a feed-in-tariff-system for the
promotion of renewable energies.
In the ‘illustrative scenario’, we analyze the value of thermal energy storage units in CSP plants in
today’s electricity market and the impact of a higher intermittent RES-E generation. CSP plants with TES
may have higher cost reductions than CSP plants without thermal storage units due to learning curve effects
in regards to the storage unit. The purpose of the ‘illustrative scenario’ is to separately analyze the effect of
an increasing share of intermittent RES-E generation on the value of TES. Thus today’s electricity system
in the Iberian Peninsula is carried forward until 2050, assuming today’s investment costs, electricity demand
as well as fuel and CO2 prices. In order to analyze the effects of an increasing share of CSP and other
fluctuating RES-E generation the following RES-E and CSP quotas are incorporated (Table 1).
Table 1: Framework of the ‘illustrative scenario’
RES-E quota
CSP quota
2020
2030
2040
2050
≥ 30 %
≥ 3.5 %
≥ 40 %
≥ 10 %
≥ 60 %
≥ 17.5 %
≥ 80 %
≥ 25 %
The role of CSP plants with and without thermal storage units in a possible transformation process
to a primarily renewable based electricity system is analyzed in the ’high RES-E scenario’. In contrast to
the ‘illustrative scenario’, only a RES-E and no CSP quota is modeled for the Iberian Peninsula (Table
2). Moreover, an increasing electricity demand is assumed and possible investment cost reductions of RESE due to learning curve effects are taken into consideration. This scenario thus incorporates two effects
potentially favoring CSP plants with storage units in the long-term: An increasing share of intermittent
RES-E generation and a decreasing cost-difference between CSP plants with and without storages.
4
Table 2: Framework of the ’high RES-E scenario’
RES-E quota
Demand
2020
2030
2040
2050
≥ 30 %
377.3 TWh
≥ 40 %
432.2 TWh
≥ 60 %
493.3 TWh
≥ 80 %
560.8 TWh
It should be noted that the scenario setting chosen is only one possible option for the Iberian Peninsula’s
electricity system and that it is neither a forecast nor the most likely outcome. We focus on the role of
thermal storage units in CSP plants used to balance the fluctuating generation of solar and wind technologies.
3.2. Electricity market model
To analyze the value of storage units in CSP plants, we use a dynamic linear investment and dispatch
model to determine the cost-minimal electricity mix for the Iberian Peninsula until 2050.2 The objective of
the model is to minimze total system costs, which include investment costs, fixed operation and maintenance
costs, variable production costs and costs due to ramping thermal power plants. The model includes possible
investments in nuclear, conventional, carbon capture and storage (CCS), storage and renewable technologies.
We model several CSP plants with and without thermal storage units in order to determine the value of
storage capacities in CSP plants.
Investment decisions are based on the dispatch requirements which are incorporated by modeling the
dispatch for three days (Saturday, Sunday and a weekday) per season on a hourly basis (scaled to 8760
hours). Three days per season are used to account for the different demand structures on weekends and
weekdays. Typical feed-in structures for each season for wind and solar technologies are modeled, including
days with both very low windpower and high-wind days. In addition, we model several wind regions within
the countries to account for different wind speeds.
3.2.1. Key model elements
The objective of the model is to minimize the total system costs (T COST ), which are defined by
investment and fixed operational and maintenance costs (F C), variable production costs which comprise
of fuel and CO2 prices (V C) for all technologies (a), countries (c) and years (y) and costs due to ramping
thermal power plants (V CRT O). Total costs are reduced by the remunerations combined heat and power
plants (CHP) can earn on the heating market (HB). All costs and earnings are inflation-adjusted and
2 The model used in this analysis is an extended version of the long-term investment and dispatch model for conventional,
storage and transmission technologies of the Institute of Energy Economics (University of Cologne) presented in Richter (2011).
The model is based on several electricity market optimization models; mainly the model developed by Bartels (2009).
5
discounted with a 5 % rate (dsc).3
"
#
X
minimize T COST =
dsc(y) · (F C(y, c, a) + V C(y, c, a) + V CRT O(y, c, a) − HB(y, c, a))
(1)
y,c,a
Investment costs occur for new investments in generation units and are annualized including a 5 % interest
rate for the depreciation time (annuity). The fixed operation and maintenance costs (f om) represent staff
costs, insurance charges, rates and fixed maintenance costs.
F C(y, c, a) = CAP ADD(y, c, a) · annuity(a) + IN ST CAP (y, c, a) · f omc(a)
(2)
The variable generation costs (V C and V CRT O) depend on the cost-minimizing dispatch. Equation 3 shows the determination of V C by fuel prices (f uelpr), CO2 price (copr), CO2 emission-factor
(emissionf ac), net efficiency (η) and the generation of all technologies (GEN ).
#
"
X
f uelpr(y, a) + copr(y) · emissionf ac(a)
V C(y, c, a) =
GEN (y, c, a, d, h) ·
η(a)
(3)
d,h
Modeling ramp-up restrictions and ramping costs of thermal power plants is difficult in linear optimization models. To fully account for technical restrictions a mixed-integer optimization is needed. However,
a mixed-integer optimization strongly increases computational time and is thus difficult to implement in
models with very detailed technological, regional and/or hourly resolutions. We simulate ramp-up costs by
referring to the power plant blocks and by setting a minimal load restriction. Depending on the minimum
load and start-up time of thermal power plants, additional costs for ramping occur (attrition (attc) and
extra fuel costs).
"
V CRT O(y, c, a) =
X
d,h
f uelpr(y, a) + copr(y) · emissionf ac(a)
CAP U P (y, c, a, d, h) ·
+ attc(a)
η(a)
#
(4)
The remunerations of CHP plants in the heating market (HB) are determined by the price on the heat
market (heatpr), the heat-to-power-ratio of CHP plants and the generation of CHP plants. Heat in cogeneration can be produced by gas, coal and lignite plants (with or without carbon capture) as well as by
biomass and geothermal units. However, only a limited amount of generation in CHP plants receives a
reward on the heating market accounting for a maximum potential for heat in co-generation within each
country. The inflexibility of CHP power plants is represented by longer ramp-up-times.
HB(y, c, a) =
X
heatpr(y) · heatratio(a) · GEN (y, c, a, d, h)
d,h
3 The
description of all model parameters and variables can be found in the Appendix A.
6
(5)
Apart from the basic cost equations the model incorporates all common elements of linear dispatch models
such as storage equations, net transfer possibilities and restrictions due to local resource availabilities (e.g.
lignite) or due to political decisions (e.g. ban of nuclear power). The availability of conventional, nuclear,
dispatchable renewable energies and storage capacities is reduced by potential breakdowns and maintenance
times.
3.2.2. Modeling renewable energies and CSP-plants
The model includes the following renewable energy technologies: roof and ground photovoltaic systems
(PV), wind (onshore and offshore), biomass (solid and gas), biomass CHP (solid and gas), geothermal,
hydro (storage and run-of-river) and CSP technologies. Biomass, geothermal and hydro technologies are
modeled as dispatchable renewables. The availability (avail) of fluctuating renewable energies (wind and
solar technologies) highly depends on weather conditions. The availability parameter represents the (maximum possible) feed-in of wind and solar sites within each hour. This approach allows the possibility of wind
and solar curtailment when needed to meet demand or when total system costs can be reduced due to lower
ramping costs of thermal power plants.4
GEN (y, c, a, d, h) ≤ avail(c, a, d, h) · IN ST CAP (y, c, a)
(6)
For wind and solar technologies, an available space potential in km2 per region is assumed. Biomass
fuels are restricted to a certain potential in MWhth (Section 4). The generation of renewable energies needs
to exceed the quota of the net electricity demand.
"
X
GEN (c, res, d, h) ≥ quota(c) ·
res,d,h
#
X
demand(c, d, h)
(7)
d,h
In concentrating solar plants the heat of the sun is absorbed by collectors and concentrated to heat a
fluid which is then used to generate electricity in a steam turbine. The heat can be saved in a storage unit
and the electricity generation can take place later. The maximum storage level is determined by the volume
factor (volf ac) which is the ratio of storage to turbine capacity.
ST O(y, c, a, d, h) ≤ volf ac(a) · IN ST CAP (y, c, a)
(8)
Equation 9 shows the power balance of the CSP system in each hour h. The injection variable (IN J)
represents the solar energy which is absorbed by the collectors. It is modeled as a variable with the restriction
4 Wind sites are usually larger than solar sites and therefore transaction costs for solar curtailment are assumed to be higher
than for wind sites. We use negligible small variable costs for offshore wind and even smaller ones for onshore wind sites.
Therefore the model chooses offshore wind curtailment first.
7
to be lower than the absorbed heat of the sun in this specific hour (sunpower) which is determined by the
size of the collector field and the efficiency for the absorption. CSP plants with storage units are able
to shift the energy (SIN ) of the absorbed sun to hours with less or no solar radiation (SOU T ). Losses
in storage processes occur due to energy consumption in pumps during charging (ηsin ) and discharging
processes (ηsout ), efficiency losses in heat exchangers and losses of stored energy over time. Efficiency losses
over time for stored energy in the TES are negligible (Sioshansi and Denholm, 2010) and therefore not
incorporated in the model.
IN J(y, c, a, d, h) + SOU T (y, c, a, d, h) · ηsout (a) − GEN (y, c, a, d, h)/ηtur (a) − SIN (y, c, a, d, h) = 0
(9)
Equation 10 shows the change of storage level (SLEV EL) from hour h to hour h + 1. The change of
storage level depends on the storage operation in the specific hour considering losses during the charging
process (ηsin ).5
SLEV EL(y, c, a, d, h + 1) − SLEV EL(y, c, a, d, h) = SIN (y, d, h, b, a) · ηsin (a) − SOU T (y, c, a, d, h) (10)
As we focus on the renewable energy generation of CSP plants in this analysis, the option of co-firing
of natural gas is not included in the model. Natural gas co-firing is another option to achieve a higher
utilization rate of the capital intensive power plant block and to increase the capacity factor of the plant.
Hence, co-firing with natural gas is in most cases an option to increase the economic value of CSP plants.
4. Assumptions
In this section, the assumptions concerning the development of electricity demand and potential heat
generation in CHP plants, the economic-technical parameters of conventional, storage and renewable technologies and the political targets are described. The analyzed concentrating solar power plants are explained
in detail.
4.1. Electricity demand and potential heat generation in CHP plants
The development of the electricity demand depends on social, economical and technological factors. This
includes the population and economic growth and the technological development. Within the next decades
a significant increase of the population and a strong development of the economy is predicted for Spain
and Portugal (Capros et al., 2010). Moreover, the introduction of new technologies as well as the greater
5 The storage level is set to 10 percent at the beginning of each model year which has to be reached in the last modeled hour
again.
8
usage of electricicty in the transportation sector are expected to lead to a higher overall electricity demand.
Therefore, we assume a strong increase in electricity consumption of 1.9 % per year until 2020, followed by
an increase of about 1.4 % per year until 2050. Table 3 shows the assumed electricity demand in Spain and
Portugal until 2050.
Table 3: Net electricity demand in TWh
Spain
Portugal
2010
2020
2030
2040
2050
265.9
50.6
298.6
55.9
344.9
64.5
396.3
74.1
453.2
84.8
Based on the assumptions regarding the development of the population and the economy, the heat
generation by CHP plants is assumed to increase as well. The slight decrease in demand for district heating
due to energy efficiency improvements is assumed to be compensated by an increase in process heat demand.
Table 4 shows the assumed heat generation by CHP plants for Spain and Portugal.
Table 4: Potential heat generation in CHP plants in TWh
Spain
Portugal
2010
2020
2030
2040
2050
57.9
13.6
59.0
13.9
59.9
14.0
60.7
14.3
61.5
14.5
The generated heat is remunerated by the assumed gas price considering an efficiency factor for the
heating system which roughly represents the opportunity costs for households and industries.
4.2. Conventional and storage technologies
Assumptions about investment costs and techno-economical characteristics of conventional power plants
and storage technologies are based on IEA (2010a) and Schlesinger et al. (2010).
The investment costs for already existing conventional power plants and storage technologies are assumed
to be the same as today but learning effects lead to lower investment costs for new technologies. These
learning costs may materialize through the deployment of improved materials and process techniques: future
hard coal plants (‘hard coal innovative’) will for example be able to run at 700 degrees celsius and higher
pressures (350 bars). Due to these improvements, the efficiency is assumed to increase by 4 % points to 50
%. Investment costs are above today’s standard technologies but are assumed to decrease due to learning
effects by about 1/3 until 2050. Future lignite technologies (‘lignite innovative’) use a more efficient drying
process and can therefore increase their efficiency to 48 %. Investment costs are just above today’s newest
9
technologies. CCS Technologies are assumed to be commercially available and applicable to hard coal, lignite
and combined-cycle gas power plants starting from 2030.
As can be seen in Table 5, standard and innovative technologies can be fitted with CCS and/or CHP
technology. The investment costs of CCS technologies decrease until 2050. The investment costs of CHP
plants also include additional costs for the grid and the extraction of heat. Due to limited space, pump
storage and hydro storage plants are not an investment option. Compressed air energy storage (CAES)
technologies have investment costs of 850 e2010 per kW.
Table 5: Investment costs of conventional and storage technologies in e2010 /kW
Technologies
2010
2020
2030
2040
2050
Hard Coal
Hard Coal - innovative
Hard Coal - CCS
Hard Coal - innovative CCS
Hard Coal - innovative CHP
Hard Coal - innovative CHP and CCS
Lignite
Lignite - innovative
Lignite - CCS
OCGT
CCGT
CCGT - CCS
CCGT - CHP
CCGT - CHP and CCS
Pump storage
Hydro storage
CAES
1,500
2,500
2,650
1,850
1,950
700
1,250
1,500
850
1,500
2,250
2,650
1,850
1,950
700
1,250
1,500
850
1,500
1,875
2,000
2,475
2,275
2,875
1,850
1,950
2,550
700
1,250
1,550
1,500
1,700
850
1,500
1,750
1,900
2,300
2,150
2,700
1,850
1,950
2,500
700
1,250
1,500
1,500
1,650
850
1,500
1,650
1,850
2,200
2,050
2,600
1,850
1,950
2,450
700
1,250
1,450
1,500
1,600
850
Table 6 shows the efficiency grades, CO2 emission factors, technical availability, operational and maintenance costs and the technical lifetime for nuclear, thermal energy and storage plants. Efficiency grades are
based on the specifications of power plants in construction. For ‘innovative’ technologies, higher efficiencies
are assumed due to the described technical developments. The generation efficiency of plants with CCS
are assumed to be lower. Moreover, higher operational and maintenance costs occur due to the additional
costs for the pipe and the storage system. Combined heat and power generation units have lower electrical
but higher total efficiency grades. Operational and maintenance costs also include the costs for the heat
extraction system.
10
Table 6: Economic-technical parameters for conventional and storage technologies
Technologies
ηgen
[%]
ηload
[%]
CO2 factor
[t CO2 /MWhth ]
avail
[%]
FOM-costs
[e 2010 /kW ]
Lifetime
[a]
Nuclear
Hard Coal
Hard Coal - innovative
Hard Coal - CCS
Hard Coal - innovative CCS
Hard Coal - innovative CHP
Hard Coal - innovative CHP and CCS
Lignite
Lignite - innovative
Lignite - CCS
OCGT
CCGT
CCGT - CCS
CCGT - CHP
CCGT - CHP and CCS
Pump storage
Hydro storage
CAES
33.0
46.0
50.0
42.0
45.0
22.5
18.5
43.0
46.5
43.0
40.0
60.0
53.0
36.0
36.0
87.0
87.0
86.0
83.0
82.0
0.000
0.335
0.335
0.034
0.034
0.335
0.050
0.406
0.406
0.041
0.201
0.201
0.020
0.201
0.030
0.0
0.0
0.0
84.50
83.75
83.75
83.75
83.75
83.75
83.75
86.25
86.25
86.25
84.50
84.50
84.50
84.50
84.50
95.00
95.00
95.00
96.6
36.1
36.1
97.0
97.0
55.1
110.0
43.1
43.1
103.0
17.0
28.2
40.0
88.2
100.0
11.5
11.5
9.2
60
45
45
45
45
45
45
45
45
45
25
30
30
30
30
100
100
40
4.3. CSP plants and other renewable technologies
CSP plants are determined by three independent components. The size of the collector’s field determines
the energy which is absorbed by the sun. Thermal energy storage units give the opportunity to shift energy
to later hours. The turbine size determines the maximum electricity that can be generated. In this analysis
three CSP plants (Table 7) are modeled based on Sioshansi and Denholm (2010). As we use a linear model
the technologies are defined by 1 MW capacity units. As explained in Section 3.2, the option of co-firing is
not considered in this analysis. Therefore, the three CSP systems are designed to generate electricity from
solar energy only.
Table 7: Modeled CSP technologies
CSP A
CSP B
CSP C
Collector surface [m2 ]
Storage volume [MWhth ]
ηf ield [%]
ηturbine [%]
ηload/unload [%]
Multiple
7,376
11,384
15,887
0
20 (7.5 h)
40 (15.0 h)
42.0
42.0
42.0
37.7
37.7
37.7
96.0/97.0
96.0/97.0
1.3
2.0
2.8
The modeled CSP technologies differ with respect to storage volume and size of collector surface. CSP
A has a collector surface of 7,376 m2 and no storage capacity. Thus the thermal energy has to be used to
generate electricity at the time it is absorbed by the sun. CSP B represents plants with an average solar field
11
of 11,384 m2 and an average storage unit of 20 MWh and CSP C has a large solar field of 15,887 m2 and a
storage unit of 40 MWh. All three CSP technologies have a common solar collector and turbine efficiency of
42 % respectively 37.7 %, but a different solar multiple, which indicates the extent to which the solar field
is over-sized in relation to the turbine capacity.6
Table 8 gives an overview of the modeled renewable energy technologies and their assumed specific
investment costs over time (based on IEA (2010c), Arens et al. (2010) and EWI (2010)). Besides CSP,
the model includes the renewable energy technlogies photovoltaics (base and roof), wind onshore, wind
offshore (deep and shallow water), biomass (solid and gas), hydro (run-of-river and storage) and geothermal
power. Investment costs are assumed to decrease over time due to learning effects. This applies especially to
photovoltaics and offshore wind. To account for technological process apart from cost reductions, we model
6 MW onshore (5 MW offshore) wind turbines until 2025 and 8 MW onshore (8 MW offshore) turbines
starting from 2030. Todays onshore wind sites are assumed to be 3 MW turbines on average.
Since the annual generation and feed-in structure of wind and solar technologies depends on local weather
conditions, it generally differs between various regions of a country. To account for these differences, the
Iberian Peninsula is divided in five solar and five wind regions.7
Table 8: Investment costs for renewable technologies in e2010 /kW
CSP A
CSP B
CSP C
Photovoltaics base
Photovoltaics roof
Wind onshore 6 MW
Wind onshore 8 MW
Wind offshore 5 MW
Wind offshore 8 MW
Wind offshore 5 MW
Wind offshore 8 MW
Biomass gas
Biomass gas - CHP
Biomass solid
Biomass solid - CHP
Hydro (run-of-river)
Geothermal power
(shallow)
(shallow)
(deep)
(deep)
2010
2020
2030
2040
2050
3,722
6,794
10,082
3,000
3,500
1,350
3,200
3,800
2,400
2,600
3,300
3,500
4,500
15,000
2,220
3,437
5,500
1,796
2,096
1,221
2,615
3,105
2,398
2,597
3,297
3,497
4,500
10,504
1,700
2,300
3,800
1,394
1,627
1,161
2,512
2,956
2,395
2,595
3,293
3,493
4,500
9,500
1,400
2,100
3,100
1,261
1,471
1,104
2,390
2,811
2,393
2,592
3,290
3,490
4,500
9,035
1,290
1,963
2,693
1,199
1,399
1,103
2,387
2,808
2,390
2,590
3,287
3,486
4,500
9,026
6 The solar multiple is defined as the ratio of the actual size of a CSP plant’s solar field compared to the field size needed to
feed the turbine at design capacity at reference solar irradiance of about 1kW/m2 (IEA, 2010b).
7 The regions are based on specific wind and solar data from Sperling and Hänsch (2009). The wind and solar regions are
not identical.
12
4.4. Fuel prices
Table 9 shows the fuel prices assumed for thermal power plants in the scenarios. The fuel prices are
based on international market prices and transportation costs to the power plants. The coal price is assumed to increase from 11.90 e2010 /M W hth in 2010 to 17.60 e2010 /M W hth in 2050. For domestic lignite
a constant price of 1.40 e2010 /M W hth is assumed. Despite the current excess supply and low prices of
natural gas we assume a significant increase up to 28.00 e2010 /M W hth in the long term. As the model
includes several biomass technologies, only a range for the price of biomass solid and gas is given in Table 9.
The price for biomass solid is assumed to increase up to 18.80-37.50 e2010 /M W hth and biomass gas up to
0-85.10 e2010 /M W hth . The price of CO2 emissions is assumed to increase from 14.00 e2010 /tCO2 in 2010
to 40.00 e2010 /tCO2 in 2050.
Table 9: Fuel prices in e2010 /MWhth and CO2 price [ e2010 /tCO2 ]
Nuclear
Coal
Lignite
Natural Gas
Biomass (solid)
Biomass (gas)
CO2 price [e2010 /tCO2 ]
2010
2020
2030
2040
2050
3.40
11.90
1.40
16.90
5.00-27.70
0-70.00
14.00
3.30
13.10
1.40
20.90
5.00-27.70
0-67.20
20.00
3.30
13.60
1.40
22.90
15.70-34.90
0-72.90
25.00
3.30
15.10
1.40
25.60
16.70-35.10
0-78.80
30.00
3.30
17.60
1.40
28.00
18.80-37.50
0-85.10
40.00
4.5. Political assumptions
Political plans for Spain and Portugal include the transformation to a primarily renewable based and low
carbon electricity system starting from 2050. In the scenarios 80 % of the electricity generation must come
from renewable energies in 2050. The ‘illustrative scenario’ additionally assumes a CSP quota to analyze
the effects of a high share of CSP generation on the value of thermal energy storage.8
Table 10: RES-E and CSP generation quotas [%]
RES-E quota [%]
CSP quota [%]
8 The
2010
2020
2030
2040
2050
-
40.0
2.5
50.0
7.5
60.0
15.0
80.0
25.0
electricity generation from CSP plants can contribute to the RES-E generation quota.
13
5. ‘Illustrative scenario’: The value of thermal storage units in CSP plants
In the ‘illustrative scenario’, we analyze the value of thermal storage units in CSP plants depending on
the share of fluctuating RES-E generation. In the future, CSP plants with thermal storage units might
have a comparative advantage compared to CSP plants with no storage capacity for two reasons: first, due
to learning curve effects of storage technologies, the cost difference between CSP plants with and without
storage capacities is likely to decrease; and secondly, the value of thermal storage capacities is likely to
increase with a higher share of fluctuating RES-E generation. For the exclusive illustration of the later
effect, i.e. the development of the value of thermal storage units as a function of the share of fluctuating
RES-E generation, today’s environment (e.g. demand or investment costs) is carried forward. This includes
the costs of CSP plants and hence the cost differences between CSP plants with and without storage
capacities are kept constant at current levels. Only the share of RES-E and CSP in particular are assumed
to significantly increase over time, by modeling both a rising RES-E (80 % in 2050) and CSP generation
quota (25 % in 2050).
5.1. Overview of the generation system
An overview of the cost-efficient capacities and gross electricity generation in the ’illustrative scenario’ for
the Iberian Peninsula until 2050 is given in Figure 1. Against the background of the implied transformation
to a renewable based electricity system, the total capacity increases until 2050. In this scenario, nuclear
is not an investment option and the combination of fuel and CO2 prices favours gas generation. Thus the
conventional generation system is dominated by gas capacities - some equipped with CHP.9 To reach the
RES-E and CSP generation quota, mainly CSP plants and wind onshore sites are built. Existing photovoltaic
capacities are not rebuilt after their technical lifetime ends.
9 The data for 2000/2008 is based on EUROSTAT (2010). CHP capacities and generation is included in gas and coal
capacities in 2000 and 2008.
14
Leistung und Erzeugung in IBERIA[GW]
GW
150
TWh
400
350
300
100
250
200
150
50
100
50
0
0
2000 2008 2020 2030 2040 2050
Nuclear
Lignite-CHP
Coal-CCS
Gas
Gas-CHP-CCS
Storage (Pump + CAES)
Geothermal
Wind onshore
CSP A
2000 2008 2020 2030 2040 2050
Lignite
Lignite-CHP-CCS
Coal-CHP
Gas-CCS
Oil
Others
Biomass
Wind offshore
CSP B
Lignite-CCS
Coal
Coal-CHP-CCS
Gas-CHP
Oil-CHP
Hydro
Biomass-CHP
PV
CSP C
Figure 1: Capacities [GW] and generation [TWh] in the ‘illustrative scenario’ (2000/2008 based on EUROSTAT (2010))
In the short-term, the electricity generation is similar to today’s electricity mix. Base load generation
takes place in nuclear, lingite and coal capacities. After 2030, the conventional generation occurs mainly in
gas-fired power plants and lignite capacities. The renewable generation is provided by CSP plants, onshore
wind turbines, biomass and hydro plants. Also, the generation in pump storages increases in the long
term. In sum, gross electricity generation decreases over time (despite the constant demand) due to the
transformation to a renewable based system. RES-E technologies apart from biomass capacities have no
own electricity consumption.
CSP plants are built in order to fullfill the increasing generation quota over time.10 In the short term
only CSP plants with no storage capacities (CSP A) are constructed. CSP plants with small storage
capacities (CSP B) with the ability to shift generation to later hours are cost efficient when the penetration
of fluctuating RES-E generation exceeds a certain limit. In this scenario, about 10 % of the CSP plants
are equipped with small storage capacities when the RES-E share reaches 80 % and when CSP generation
makes up 25 % of total generation. The installed capacities of CSP plants are shown in Table 11.
10 The
CSP generation quota is binding in all years.
15
Table 11: Installed capacities of CSP technologies in GW
CSP A
CSP B
CSP C
2010
2015
2020
2025
2030
2035
2040
2045
2050
0.5
0.4
0.0
3.1
0.4
0.0
4.6
0.4
0.0
10.6
0.4
0.0
14.4
0.4
0.0
18.7
0.0
0.0
21.1
3.4
0.0
24.8
3.4
0.0
32.3
3.4
0.0
5.2. The value of thermal storage units in CSP plants
The model results are based on the favorable feed-in structures of solar technologies in order to meet
electricity demand. CSP plants and photovoltaics generate electricity when demand is usually high. Hence,
at a low penetration of fluctuation RES-E, there is no benefit from having additional storage capacities and
being able to shift electricity generation to later hours. Therefore thermal storage units in CSP plants are
not cost-efficient in electricity systems in the short-term. Figure 2 shows the feed-in structures of fluctuating
generation technologies (wind, solar photovoltaic and CSP plants), the model demand, and the marginal of
the power balance for the example of the Spanish electricity market.11
The marginal on the power balance can be interpreted as the value of generation in a specific hour.
In general, high generation by technologies with negligible variable generation costs such as wind or solar
technologies lead to a lower marginal of the power balance. As can be seen in Figure 2, the marginal of the
power balance is primarily influenced by the height of the model demand. Fluctuating renewables play a
minor role in the short-term because generation of wind turbines, photovoltaics or CSP plants is relatively
low compared to the demand.
11 The equilibrium condition ‘power balance’ assures the balance of electricity generation and demand in each hour. The
marginal of the power balance represents the partial derivative considering the total system costs.
16
Power
[GW]
Marginal
[€/MW]
Low penetration of fluctuating RES-E
40
100
80
30
60
20
40
10
20
0
0
SAT
SUN
WED
SAT
Spring
CSP A
SUN
WED
SAT
Summer
CSP B
CSP C
SUN
WED
SAT
Autumn
Wind
PV
SUN
WED
Winter
Model demand
Marginal
Figure 2: Low fluctuating RES-E penetration
Figure 3 shows the development of renewable generation and the marginal in an electricity system with a
medium penetration of fluctuating RES-E. The increasing generation of fluctuating renewable technologies
leads to a more volatile marginal. As can be seen, additional CSP capacities (CSP A) with a relatively high
generation at midday cause relatively low marginals on the power balance in these specific hours.
Power
[GW]
Marginal
[€/MW]
Medium penetration of fluctuating RES-E
40
100
80
30
60
20
40
10
20
0
0
SAT
SUN
WED
SAT
Spring
CSP A
SUN
WED
SAT
Summer
CSP B
CSP C
SUN
WED
SAT
Autumn
Wind
PV
SUN
WED
Winter
Model demand
Marginal
Figure 3: Medium fluctuating RES-E penetration
The influence of CSP generation on the marginal increases significantly due to the concentrated genera17
tion at midday in this scenario. Around midday when generation by solar technologies is high, the marginal
on the power balance - especially in the summer - is starting to be even lower than at night. The structure
of the marginal is almost reverse compared to today (especially in the summer): lower marginals when
electricity demand is high around midday and higher marginals when electricity demand is low by night.
Figure 4 shows the value of CSP storage units in a high fluctuating RES-E scenario. A high share of
fluctuating RES-E capacities - especially solar - leads to a low value of additional generation around midday.
Therefore the value of storage options in CSP plants increases. This leads to investments in CSP plants with
small storage capacities (CSP B) in order to shift generation to later hours. The CSP plants with storage
units are able to balance the generation from fluctuating wind and CSP plants without storage units (CSP
A) and the demand.
Power
[GW]
Marginal
[€/MW]
High penetration of fluctuating RES-E
40
100
30
50
20
0
10
-50
50
0
-100
SAT
SUN
WED
SAT
Spring
CSP A
SUN
WED
SAT
Summer
CSP B
CSP C
SUN
WED
SAT
Autumn
Wind
PV
SUN
WED
Winter
Model demand
Marginal
Figure 4: High fluctuating RES-E penetration
From the results of the ‘illustrative scenario’, we draw the following conclusions: first, investments in
CSP plants with storage units in today’s electricity systems of Spain and Portugal are not cost-efficient from
a system integrated viewpoint. The growing investments in CSP plants with storage units in the Spanish
market result from the specific design of the Spanish RES-E promotion system and do not reflect investment
signals of the competitive electricity market, which would favor CSP plants without storage units. Second,
we come to the conclusion that the value of storage units in CSP plants increases when the share of electricity
generation by CSP plants without storage units and other intermittent RES-E increases. However, the share
of intermittent RES-E has to reach a substantial magnitude and cause an almost reverse structure of the
18
marginal on the power balance compared to today, until CSP plants with storage units become cost efficient.
6. ‘High RES-E scenario’: The role of CSP plants in a high RES-E scenario for the Iberian
Peninsula
In this scenario, we analyze the role of CSP plants and thermal storage units in a possible transformation
to a low-carbon and mainly renewable based electricity system of the Iberian Peninsula. As described in
the Section 3, at least 80 percent of the electricity consumption has to be generated by renewable capacities
starting from 2050 (40 % in 2020). In contrast to the ‘illustrative’ scenario, no lower limit for the generation
of CSP plants is modeled. The model results are based on the assumptions described in Section 4. Other
assumptions (e.g. other fuel prices) may lead to a different cost-minimal electricity mix.
6.1. Capacities and generation mix
The implied transformation of the electricity system results in a large extension of RES-E capacities
until 2050. The generation of fluctuating RES-E depends on weather conditions and therefore the maximum
yearly generation per unit is lower compared to conventional power plants. Due to this effect the sum of
capacities increases significantly. The demand in 2050 is twice as high as in 2000 but generation capacities
triple until 2050. Figure 5 shows the installed capacities and generation in this scenario.12
To achieve the implied RES-E generation quota mainly wind onshore sites are expanded (retrofit options
are taken as well) and biomass capacities are used in the short term. Starting in 2020 CSP technologies
with small storage capacties (CSP B) are constructed. Due to the scenario assumptions, the model chooses
CSP systems over photovoltaics. In the long term larger CSP plants with 15 hours of storage capacity (CSP
C) have a comparative cost advantage compared to smaller CSP plants. Additionally, the value of thermal
storage units in CSP plants increases in higher RES-E scenarios as shown in the ‘illustrative scenario’.
The assumptions concerning the conventional generation technologies, fuel prices and flexibility requirement of the power plant mix lead to a gas-dominated conventional generation system. Lignite and hard coal
capacities (often equipped with CHP technology) replace nuclear capacities as base load generation.
12 The
power balances for Spain and Portugal are shown in Appendix B.
19
Leistung und Erzeugung in IBERIA[GW]
GW
200
TWh
600
500
150
400
100
300
200
50
100
0
0
2000 2008 2020 2030 2040 2050
Nuclear
Lignite-CHP
Coal-CCS
Gas
Gas-CHP-CCS
Storage (Pump + CAES)
Geothermal
Wind onshore
CSP A
2000 2008 2020 2030 2040 2050
Lignite
Lignite-CHP-CCS
Coal-CHP
Gas-CCS
Oil
Others
Biomass
Wind offshore
CSP B
Lignite-CCS
Coal
Coal-CHP-CCS
Gas-CHP
Oil-CHP
Hydro
Biomass-CHP
PV
CSP C
Figure 5: Capacities [GW] and generation by fuels [TWh] (2000/2008 based on EUROSTAT (2010))
6.2. The usage of storage units in high RES-E scenarios
A higher generation by fluctuating RES-E technologies leads to a more volatile residual electricity demand. This requires a higher share of flexible conventional generation such as combined cycle or open cycle
gas turbines to balance generation and demand. The costs of ramping thermal power plants rises with high
generation by fluctuating RES-E capacities.
Figure 6 shows the model demand (black line), the model demand after subtracting the generation by
fluctuating RES-E (blue line), the model demand after subtracting the generation by fluctuating RES-E
and storage operations (yellow line) as well as the final residual demand (green line), which has to be met
by thermal power plants for the Iberian Peninsula in 2020. The system is characterized by 10 % electricity
generation by fluctuating RES-E and large hydro capacities (hydro and pump storage). Due to the large
storage capacities, the residual demand is relatively constant compared to the model demand. As a result,
quick changes of the generation of thermal power plants is rarely needed. However, a high generation by
wind technologies in autumn leads to a more volatile residual demand, the usage of storage capacities in
pump operation (yellow line is above the green line in Figure 6) and costs for ramping thermal power plants.
20
Power
[GW]
60
40
20
0
SAT
SUN
WED
SAT
Spring
SUN
WED
SAT
Summer
SUN
WED
SAT
Autumn
Demand
Demand subtracting fluc. RES-E and CSP B/C
SUN
WED
Winter
Demand subtracting fluc. RES-E
Demand subtracting fluc. RES-E, CSP and storage
Figure 6: Different residual demands [GW] for the Iberian Peninsula in 2020
Figure 7 shows different residual demands for the Iberian Peninsula in 2050. A higher share of fluctuating
RES-E technologies would lead to a more volatile residual demand and higher costs for ramping thermal
power plants. This is observable by comparing the demand after subtracting the fluctuating RES-E generation in 2020 (blue line in Figure 6) with the demand after subtracting the fluctuating RES-E generation in
2050 (blue line in Figure 7).
Power
[GW]
100
80
60
40
20
0
SAT
SUN
WED
SAT
SUN
WED
SAT
Spring
Summer
Demand
Demand subtracting fluc. RES-E and CSP B/C
SUN
WED
SAT
SUN
WED
Autumn
Winter
Demand subtracting fluc. RES-E
Demand subracting fluc. RES-E, CSP and storage
Figure 7: Different residual demands [GW] for the Iberian Peninsula in 2050
21
CSP technologies with storage units are able to shift generation from one hour to another and can
therefore help balancing generation and demand. In connection with other storage capacities (hydro and
pump storage) the residual demand can kept more or less constant in most hours. In the ‘high RES-E
scenario’ CSP technologies with storage units are rather built than additional wind technologies for several
reasons: the potential for cost-efficient onshore wind sites is limited, the investment costs for CSP plants with
storage capacities decrease significantly until 2050 and the value of storage capacities increases as shown
in the ‘illustrative scenario’. As can be seen in Figure 7, CSP plants with the ability to shift electricity
generation lead to a smoother residual demand even considering the higher RES-E generation in 2050.
7. Conclusions
We have shown that thermal energy storage units in CSP plants in today’s electricity systems of Spain
and Portugal are not cost-efficient from a system integrated viewpoint due to the relatively high demand at
midday when solar radiation is also highest. Therefore, flat feed-in-tariffs lead to an inefficient generation
mix as tariffs set an incentive to install thermal energy storage units in CSP plants which can reduce average
generation costs and hence maximize profits. The value of TES in CSP plants increases with a higher share
of wind and solar generation as storage technologies can help balancing fluctuating generation and demand.
Due to specific learning curve effects in regard to the thermal storage unit, the cost difference between CSP
plants with and without thermal storage is likely to decrease. As also shown, CSP plants play a potentially
significant role in a transformation to a primarily renewable based electricity system.
The analysis approach could be improved and extended in several ways. It would be desirable to include
co-firing of natural gas as another option to fully understand the value of storage units in CSP plants. In
addition, a more realistic mapping of the electricity system could be achieved by modelling transmission
constraints. It would also be interesting to analyze the effects of different locations for energy storages
on transmission requirements, which are expected to be the lower the closer the energy storage is located
to the (solar) power plant (Denholm and Sioshansi, 2009). Due to the neglection of uncertainty, forecast
errors of wind and solar power or short notice power plant outages are not included in the model. Therefore,
additional balancing services by thermal storage units in CSP plants are not fully considered. However, Black
and Strbac (2006) or Sioshansi and Denholm (2010) show that it is preferable to integrate the balancing
markets. The impact of uncertainty and balancing services on the value of thermal energy storages in CSP
plants or other storage options from a system integrated viewpoint provides an interesting area of further
research.
22
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23
Appendix
7.1. Appendix A - Abbreviations
Table 12: Abbreviations including model parameters and variables
Abbreviation
General
CSP
TES
RES-E
CCS
CHP
PV
Model parameters
dsc
annuity
fomc
fuelpr
copr
emissionfac
η
attc
heatpr
heatratio
avail
quota
demand
volfac
ηsout
ηsin
Model variables
TCOST
FC
VC
VCRTO
HB
INSTCAP
CAPADD
GEN
CAPUP
Dimension
Description
Concentrating solar power
Thermal energy storage
Renewable energy sources - electricity
Carbon capture and storage
Combined heat and power
Photovoltaic
%
e 2010 /kW
e 2010 /kW
e 2010 /MWhth
e 2010 /t CO2
t CO2 /MWhth
%
e 2010 /MWhel
e 2010 /MWhth
MWhth /MWhel
%
%
MW
MWh/MW
%
%
Discount rate
Annuity for technology specific investment costs
Fixed operation and maintenance costs
Fuel costs
Costs for CO2 emissions
CO2 emissions per fuel consumption
Net efficiency
Attrition costs for ramp-up operation
Heating price for end-consumers
Ratio for heat extraction
Availability of generation units
Quota on RES-E generation
Model demand
Ratio of storage size and turbine capacity
Net efficiency of storage in discharging operation
Net efficiency of storage in charging operation
e 2010
e 2010
e 2010
e 2010
e 2010
MW
MW
MWhel
MW
Total system costs
Fixed costs
Variable costs
Variable costs due to ramp-up operation
Revenues from heating generation
Installed capacity
Commissioning of new power plants
Electricity generation
Ramped-up capacity
24
7.2. Appendix B - CSP projects in Spain based on NREL (2011)
Table 13: CSP projects in Spain based on NREL (2011)
Project
Alvarado I
Andasol-1 (AS-1)
Andasol-2 (AS-2)
Andasol-3 (AS-3)
Andasol-4 (AS-4)
Arcosol 50 (Valle 1)
Central Solar Termoelectrica La Florida
EL REBOSO II 50-MW
EL REBOSO III 50-MW
Extresol-1 (EX-1)
Extresol-2 (EX-2)
Extresol-3 (EX-3)
Gemasolar Thermosolar Plant (Gemasolar)
Helios I (Helios I)
Helios II (Helios II)
Ibersol Ciudad Real (Puertollano)
La Dehesa
Lebrija 1 (LE-1)
Majadas I
Manchasol-1 (MS-1)
Manchasol-2 (MS-2)
Palma del Rı́o I
Palma del Rı́o II
Planta Solar 10 (PS10)
Planta Solar 20 (PS20)
Puerto Errado 1 Thermosolar Power Plant
Puerto Errado 2 Thermosolar Power Plant
Solnova 1
Solnova 3
Solnova 4
Vallesol 50 (Valle 2)
Start Production
Turbine [MW]
Solar-Field [m2 ]
Storage [h]
2009
2008
2009
2011
2020
2010
2010
2011
2012
2010
2010
2010
2010
n.a.
n.a.
2009
2011
2010
2010
2011
2010
2011
2010
2007
2009
2009
2012
2009
2009
2009
2020
50
50
50
50
50
49.9
49.9
50
50
50
49.9
49.9
17
49.9
49.9
50
49.9
49.9
50
49.9
49.9
50
50
11.02
20
1.4
30
50
50
50
49.9
n.a.
510,120
510,120
n.a.
510,120
n.a.
552,750
319,057
518,469
510,120
510,120
510,210
318,000
n.a.
n.a.
287,760
552,750
412,020
n.a.
510,120
510,120
n.a.
n.a.
75,000
150,000
n.a.
n.a.
300,000
300,000
300,000
510,120
0
7.5
7.5
7.5
7.5
7.5
7.5
0
2.3
7.5
7.5
7.5
15.0
0
0
0
7.5
0
0
7.5
7.5
0
0
1.0
1.0
n.a.
n.a.
0
0
0
7.5
25
7.3. Appendix C - Power balance of Spain and Portugal in the ’high RES-E scenario’
Table 14: ‘High RES-E scenario’ - Power balance for Spain in TWh (2000 and 2008 based on Eurostat (2010))
Net electricity consumption
Transformation losses
Thermal plant consumption
other transformation
Grid losses
Storage consumption
Gross electricity consumption
Net imports
Gross electricity generation
2000
2008
2020
2030
2040
2050
188.5
19.0
14.0
5.0
20.0
2.6
230.1
4.4
225.6
265.4
20.0
15.0
5.0
16.0
1.1
302.5
-11.0
313.5
298.6
27.1
22.2
5.0
13.5
4.4
342.7
-0.4
344.1
344.9
26.1
21.1
5.0
13.5
2.6
387.1
-0.8
387.8
396.3
18.4
15.9
5.0
13.5
1.7
432.4
1.3
431.1
453.2
14.7
9.2
5.0
13.5
1.5
482.4
-0.7
483.1
Table 15: ‘High RES-E scenario’ - Power balance for Portugal in TWh (2000 and 2008 based on Eurostat(2010))
Net electricity consumption
Transformation losses
Thermal plant consumption
other transformation
Grid losses
Storage consumption
Gross electricity consumption
Net imports
Gross electricity generation
2000
2008
2020
2030
2040
2050
38.5
2.3
1.7
0.6
3.6
0.2
44.6
0.9
43.7
48.4
2.4
1.8
0.6
4.2
0.2
55.2
9.4
46.0
55.9
3.6
3.0
0.6
3.8
0.9
64.1
0.2
63.9
64.5
3.4
2.8
0.6
3.8
0.8
72.5
0.7
71.8
74.1
5.4
4.8
0.6
3.8
1.0
84.2
-1.4
85.7
84.8
3.7
3.1
0.6
3.8
0.2
92.5
0.6
91.9
26